Approach to Part using Deformable Part Model in Pedestrian Detection System

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초록

Histogram of Oriented Gradient (HOG) proposed by Dalal and Triggs is currently the most basic algorithm to detection pedestrian. The algorithm is weak to occlusion, since the algorithm trained by the image of pedestrian full body images as one feature. As a result, the detection rate using HOG feature becomes decreases remarkably. To solve this problem, the paper proposed detection system using Deformable Part-based Model (DPM) just divided two parts of pedestrian data through latent Support Vector Machine (SVM) based machine learning. Experimental results show that proposed approach achieves better performance on detection with high accuracy than existed method [1].

키워드

Pedestrian Detection; Deformable Part Model; Histogram of Oriented Gradients; Object Detection
제목
Approach to Part using Deformable Part Model in Pedestrian Detection System
저자
Choi, Hye Ji; Shin, Nara; Choi, Kwang Nam
DOI
10.1117/12.2242984
발행일
2016-05
유형
Proceedings Paper
저널명
FIRST INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION
권
0011